Despite the growing interest in unveiling the effect of attributions on L2 learning, there is a dearth of literature on the relationships between attributions and other motivational factors to better explain the motivation of second language (L2) learners. To fill this gap, this study examined the antecedents (language mindsets) and consequences (learning engagement) of students’ attributions to their writing achievement with a sample of 242 second-year students from a Chinese high school. Structural equation modeling results demonstrated the positive associations between growth mindset and internal attributions (i.e., ability and effort) and between fixed mindset and external attributions (i.e., context and luck) to their writing achievement. Further, internal attributions were positively associated with behavioral, emotional, and cognitive engagement, whereas external attributions were negatively related to these three types of learning engagement, with context attribution showing no relationship with behavioral engagement. In terms of the indirect effect, only one path from fixed mindset to emotional engagement via luck attribution was evident. The study contributes to the literature by exploring the nuanced relationships between language mindsets, attributions, and learning engagement in L2 writing learning, based on which pedagogical implications and suggestions for future research are proposed.

Over the past few decades, artificial intelligence (AI) has undergone exponential growth and has been overwhelmingly permeated in the educational field, including English language education. Many individual studies have paid close attention to probing the effect of AI on learning. However, no quantitative meta-analysis has been conducted on the overall effectiveness of AI on English language learning achievement. Hence, to fill the research gap and strengthen the statistical power, this article aims to carry out a meta-analysis for examining the effectiveness of AI on English learning outcomes. A total of 40 empirical studies with 3290 participants across ten countries filtered from five academic electronic databases, yielding 55 effect sizes. Via Comprehensive Meta-Analysis (CMA) software, the results found that AI had a high effect size (g = 0.812) on English language learning achievement, indicating that students who integrated AI significantly outperformed their counterparts who followed traditional pedagogy in English achievement. Additionally, the moderating effect of ten categorical variables (i.e. nation development level, whether undergoing the COVID-19 pandemic, sample size, learning phases, students' majors, sub-fields of English learning, AI applications, intervention duration, research design, research settings) was examined. It found that sample size, learning phases and students’ majors significantly moderated the effectiveness of AI. Confronting the results, potential reasons behind them are discussed, and practical and research implications are proposed.

The concept of Questionable Research Practices (QRPs) has recently gained recognition in the field of applied linguistics. This field, which previously differentiated between macro- and micro-ethics, is now grappling with QRPs that originate from the core of microethics. Despite the importance of studying QRPs, the applied linguistics community has not paid due attention to them. The present mixed-methods study investigates the frequency and severity of QRPs among Iranian applied linguists. Additionally, it explores QRPs related to artificial intelligence (AI) tools, a topic not previously examined, using researcher-constructed scenarios. A total of 160 Iranian applied linguists participated in the study, completing a 48-item questionnaire adapted from Larsson et al. (2023). Fifteen participants also took part in follow-up scenario-based interviews with regard to AI-related QRPs. The most common QRPs identified were the selection of variables for convenience and P-hacking, while the most severe ones were plagiarism-like behaviours and authorship issues. Lack of AI-related regulations, excessive use of AI as a threat, AI disclosure dilemma, and Al deficiencies and human oversight were the themes representing AI-related QRPs. The study concludes that QRPs among Iranian applied linguists mainly stem from inadequate ethics training, limited statistical knowledge, and a lack of AI guidelines.

What seems to be overlooked in current higher education research is the perspective of individual doctoral students in applied linguistics and how they comprehend the academic training culture within U.S. neoliberal universities. Through an autoethnographic (AE) approach, this study investigated how I as a Chinese doctoral student experienced different stages of identity negotiation in my doctoral program. The findings, which integrated theories of academic identity, the third space, and neoliberalism, were primarily derived from my self-reflection data throughout the pandemic. The arguments center around the coexistence of multiple identities and an asynchronous process of identity construction, which were influenced and shaped by study-work balance, laissez-faire supervision, the digital third space, and the pressures from neoliberal norms. The study brings insights into theoretical models from an AE perspective and recommends that higher education stakeholders adopt a flexible approach to doctoral supervision and academic publishing. Autoethnography offered a first-person perspective for in-depth meaning-making, facilitating a comprehensive and nuanced understanding of one’s academic journey of becoming.

Teacher motivation is pivotal in creating engaging teaching and learning environments, enhancing students' learning outcomes, and is fundamental to teachers' professional commitment, psychological well-being, and resilience. Despite the increasing interest in EFL teacher motivation, there remains a considerable lack of in-depth exploration into the socio-political environments that shape and influence it. This qualitative study addresses this gap by exploring the link between language teacher motivation and emotion from a socio-political perspective. Focusing on three Chinese EFL teachers, the research utilized data from three rounds of semi-structured interviews, reflective journals, and documents. Using a data-driven inductive approach, the analysis revealed three types of motivation: relational, bumpy, and adaptive, influenced by two feeling rules, namely, “Don't show negative emotions” and “Do show positive emotions.” The study discusses the potential and limitations of emotion labor as a motivator in language education and a richer understanding of feeling rules. Pedagogical implications are also discussed.

In the evolving landscape of second language (L2) education, Intelligent Computer-Assisted Language Learning (ICALL) stands out as a transformative force with the emergence of Artificial Intelligence (AI). Despite its growing prominence, integrating cognitive and affective constructs such as academic emotion regulation and mindfulness remains underexplored in ICALL environments. This study addresses this gap by examining their interplay with L2 learning experience, academic motivation, and learner autonomy among Iranian English as a Foreign Language (EFL) learners. Drawing on data from 398 intermediate EFL learners, this research utilized a comprehensive array of validated instruments, including the Academic Emotional Regulation Questionnaire, the Langer Mindfulness Scale, the L2 Learning Experience Scale, the Academic Motivation Scale, and the Learner Autonomy Questionnaire. Structural equation modeling (SEM) revealed significant correlations, indicating that academic emotion regulation positively influences L2 learning experience, academic motivation, and learner autonomy within ICALL settings. Furthermore, academic mindfulness emerged as a robust predictor of these educational outcomes in ICALL environments. These findings underscore the pivotal role of ICALL in L2 education and offer practical insights for teachers, curriculum developers, and policymakers to enhance teaching and learning practices.

Interaction is an indispensable part of language learning. Artificial intelligence (AI) has been increasingly applied in language learning to promote interaction in the learning process. In response to the paradigmatic shifts in AI application design, this review maps the research landscape of language learning development in human-AI interaction. From the resulting analysis of 49 studies, this study investigates the contextual characteristics by AI-supported interaction type, AI application, target language, educational level, etc. Moreover, three research paradigms are identified in this emerging field, i.e., Paradigm One (AI-directed, teacher-as-facilitator, learner-as-recipient), Paradigm Two (AI/teacher-codirected, learner-as-collaborator), and Paradigm Three (AI/teacher/learner-codirected). The paradigms are induced through analysis of eight constructs: human-AI relationship, learning objective, task type, level of pre-structuring, mode of engagement behavior, knowledge-change process, cognitive outcome, and research focus. The philosophical and linguistic underpinnings for each paradigm are discussed. Additionally, we highlight future research implications including investigating under-researched themes and exploring diverse methodological possibilities and appropriateness among the three research paradigms.

The global spread of EMI is a response to the internationalisation and marketisation of higher education. However, students may not fully benefit from EMI programmes if they are not adequately prepared. The extent to which students have been prepared to succeed in such programmes and acquire English as linguistic capital is an underexplored area. The present study addressed the research gap by investigating the preparation and support that students received in EMI programmes in Chinese higher education. The study adopted a mixed-methods design by collecting responses to an online survey from 248 students in different Chinese universities and conducting semi-structured interviews with 12 students regarding their preparation and support for, their learning motivation and views of EMI programmes as well as their expectations of EMI teachers. The findings showed that the students did not seem to be sufficiently prepared to achieve the dual goal of EMI (i.e., subject and language learning) and that the EMI programmes tended to widen the gap between students with different English proficiency and might lead to educational inequality. Finally, the students were critical of the quality of EMI teachers and favoured teachers with high English proficiency and pedagogical skills in delivering sophisticated content knowledge. These findings suggested that future studies need to explore strategies to better prepare and support students to benefit from EMI programmes and promote educational equality. Furthermore, the criteria for recruiting EMI teachers should be reconsidered to meet students’ learning needs and development.